Model predictive control (MPC) techniques use one or more models to predict the future behavior of an industrial process. Control signals for adjusting the industrial process are then generated based on the predicted behavior. MPC techniques have become widely accepted in various industries, such as the oil and gas, pulp and paper, food processing, and chemical industries. One benefit of using MPC techniques is that a process' physical constraints can be explicitly incorporated into a controller's design, which is a challenging task for traditional control schemes like proportional-integral-derivative (PID), Dahlin, and linear-quadratic-Gaussian (LQG)/linear-quadratic regulator (LQR) techniques.
Unfortunately, MPC techniques usually formulate feedback control into a constrained optimization problem, and this is often a completely new concept for many end users. Also, the performance of an MPC controller often depends on the choice of weighting matrices used in the controller's objective functions. In many cases, the weighting matrices do not have any physical meaning to the end users. In addition, the selection of appropriate values in the weighting matrices is typically a non-trivial task, often requiring the end users to rely on MPC tuners or trial-and-error simulations.
As a result, configuring an MPC controller is often non-intuitive and requires a certain level of control knowledge to achieve success. These challenges can limit MPC acceptance in some industries or applications, such as paper-making machine direction (MD) and cross direction (CD) control. In general, it can be a challenge to educate end users to switch from traditional control schemes to MPC control schemes.